Genetic network inference methods using random forests have shown promise. Some of the random-forest-based inference methods have an ability to analyze both time-series and static gene expression data. We think however that, as the gene expression levels at two adjacent measurements of a time-series data are often similar to each other, the gene expression levels at each measurement in the time-series data are less informative than those in the static data. On the basis of this idea, we proposed a new inference method that relies more on static gene expression data than time-series ones. Through the numerical experiments, we showed that the quality of the inferred genetic network is slightly improved by giving greater importance to static data than time-series ones. Although we develop the new method by modifying the random-forest-based inference method proposed by the authors, we could introduce the idea in this study into any inference method that is capable of analyzing both time-series and static gene expression data.
{"title":"[Regular Paper] Inference of Genetic Networks Using Random Forests: Use of Different Weights for Time-Series and Static Gene Expression Data","authors":"Shuhei Kimura, M. Tokuhisa, Mariko Okada","doi":"10.1109/BIBE.2018.00026","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00026","url":null,"abstract":"Genetic network inference methods using random forests have shown promise. Some of the random-forest-based inference methods have an ability to analyze both time-series and static gene expression data. We think however that, as the gene expression levels at two adjacent measurements of a time-series data are often similar to each other, the gene expression levels at each measurement in the time-series data are less informative than those in the static data. On the basis of this idea, we proposed a new inference method that relies more on static gene expression data than time-series ones. Through the numerical experiments, we showed that the quality of the inferred genetic network is slightly improved by giving greater importance to static data than time-series ones. Although we develop the new method by modifying the random-forest-based inference method proposed by the authors, we could introduce the idea in this study into any inference method that is capable of analyzing both time-series and static gene expression data.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125162454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Title Page i","authors":"","doi":"10.1109/bibe.2018.00001","DOIUrl":"https://doi.org/10.1109/bibe.2018.00001","url":null,"abstract":"","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125761682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Grünewald, David Kroenert, Jonas Poehler, R. Brück, Frédéric Li, Julian Littau, Katrin Schnieber, A. Piet, M. Grzegorzek, Henrik Kampling, Björn Niehaves
Emotion recognition is a increasingly popular topic because of its potential applications in the field of affective learning. It allows the development of systems able to adapt themselves to the users' emotional state to improve the learner's experience and learning. In this paper, we introduce a new biomedical multi-sensor platform for realtime acquisition of physiological data comprising Temperature, Electroencephalography (EEG), Electroocculography (EOG), Galvanic Skin Response (GSR), Heart Rate and Blood Oxygen Saturation. We describe experimental scenarios for the induction of emotions relevant in a context of affective learning (happiness, frustration, boredom) to build a set of emotionrelated data. We carry out a basic classification study by computing hand-crafted features on the time and frequency domains of signals, and training a Support-Vector-Machine (SVM) classifier to demonstrate the feasibility of our approach.
{"title":"[Regular Paper] Biomedical Data Acquisition and Processing to Recognize Emotions for Affective Learning","authors":"A. Grünewald, David Kroenert, Jonas Poehler, R. Brück, Frédéric Li, Julian Littau, Katrin Schnieber, A. Piet, M. Grzegorzek, Henrik Kampling, Björn Niehaves","doi":"10.1109/BIBE.2018.00031","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00031","url":null,"abstract":"Emotion recognition is a increasingly popular topic because of its potential applications in the field of affective learning. It allows the development of systems able to adapt themselves to the users' emotional state to improve the learner's experience and learning. In this paper, we introduce a new biomedical multi-sensor platform for realtime acquisition of physiological data comprising Temperature, Electroencephalography (EEG), Electroocculography (EOG), Galvanic Skin Response (GSR), Heart Rate and Blood Oxygen Saturation. We describe experimental scenarios for the induction of emotions relevant in a context of affective learning (happiness, frustration, boredom) to build a set of emotionrelated data. We carry out a basic classification study by computing hand-crafted features on the time and frequency domains of signals, and training a Support-Vector-Machine (SVM) classifier to demonstrate the feasibility of our approach.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125819541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
According to the communication channel differences, auxiliary medical devices can be divided into wearable medical devices and implantable medical devices. For wearable medical devices, the channel error rate is analyzed by analogy method. It is found that the logarithmic distribution model is more consistent with the actual situation. Then, based on the analyzing of error generation mechanism, the formula for calculating error rate of implantable medical devices is obtained. The accuracy of the formula is verified by the comparison of simulation and experiment. The biological channel error rate of auxiliary medical devices is analyzed in this paper, which provide a theoretical reference for auxiliary medical devices' clinical application.
{"title":"Study on the Channel Characteristics of Auxiliary Medical Devices Based on MDAPSK Technology","authors":"Xueping Li, Yuan Yu, N. Yu","doi":"10.1109/BIBE.2018.00025","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00025","url":null,"abstract":"According to the communication channel differences, auxiliary medical devices can be divided into wearable medical devices and implantable medical devices. For wearable medical devices, the channel error rate is analyzed by analogy method. It is found that the logarithmic distribution model is more consistent with the actual situation. Then, based on the analyzing of error generation mechanism, the formula for calculating error rate of implantable medical devices is obtained. The accuracy of the formula is verified by the comparison of simulation and experiment. The biological channel error rate of auxiliary medical devices is analyzed in this paper, which provide a theoretical reference for auxiliary medical devices' clinical application.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130333831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peixi Li, Y. Benezeth, Keisuke Nakamura, R. Gomez, Chao Li, Fan Yang
Conventional contact photoplethysmography (PPG) sensors are not suitable in situations of skin damage or when unconstrained movement is required. As a consequence, remote photoplethysmography (rPPG) has recently emerged because it provides remote physiological measurements without expensive hardware and improves comfort for long term monitoring. RPPG estimation methods use the spatially averaged RGB values of pixels in a Region Of Interest (ROI) to generate a temporal RGB signal. The selection of ROI is a critical first step to obtain reliable pulse signals and must contain as many skin pixels as possible with a low percentage of non-skin pixels. In this paper, we experimentally compare seven ROI segmentation methods in the perspective of heart rate (HR) measurements with dedicated metrics. The algorithms are compared using our in-house database UBFC-RPPG, comprising of 53 videos specifically geared towards rPPG analysis.
{"title":"Comparison of Region of Interest Segmentation Methods for Video-Based Heart Rate Measurements","authors":"Peixi Li, Y. Benezeth, Keisuke Nakamura, R. Gomez, Chao Li, Fan Yang","doi":"10.1109/BIBE.2018.00034","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00034","url":null,"abstract":"Conventional contact photoplethysmography (PPG) sensors are not suitable in situations of skin damage or when unconstrained movement is required. As a consequence, remote photoplethysmography (rPPG) has recently emerged because it provides remote physiological measurements without expensive hardware and improves comfort for long term monitoring. RPPG estimation methods use the spatially averaged RGB values of pixels in a Region Of Interest (ROI) to generate a temporal RGB signal. The selection of ROI is a critical first step to obtain reliable pulse signals and must contain as many skin pixels as possible with a low percentage of non-skin pixels. In this paper, we experimentally compare seven ROI segmentation methods in the perspective of heart rate (HR) measurements with dedicated metrics. The algorithms are compared using our in-house database UBFC-RPPG, comprising of 53 videos specifically geared towards rPPG analysis.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130500782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multiple sequence alignment (MSA) is one of the best studied problems in bioinformatics because of the broad set of genomics, proteomics, and evolutionary analyses that rely on it. Yet the problem is NP-hard and existing heuristics are imperfect. Reinforcement learning (RL) techniques have emerged recently as a potential solution to a wide diversity of computational problems, but have yet to be applied to MSA. In this paper, we describe RLALIGN, a method to solve the MSA problem using RL. RLALIGN is based on Asynchronous Advantage Actor Critic (A3C), a cutting-edge RL framework. Due to the absence of a goal state, however, it required several important modifications. RLALIGN can be trained to accurately align moderate-length sequences, and various heuristics allow it to scale to longer sequences. The accuracy of the alignments produced is on par with, and often better than those of well established alignment algorithms. Overall, our work demonstrates the potential of RL approaches for complex combinatorial problems such as MSA. RLALIGN will prove useful for realignment tasks, where portions of a larger alignment need to be optimized. Unlike classical algorithms, RLALIGN is incognizant to the nature of the scoring scheme, leading to easy generalization to a variety of problem variants.
{"title":"RLALIGN: A Reinforcement Learning Approach for Multiple Sequence Alignment","authors":"R. Ramakrishnan, Jaspal Singh, M. Blanchette","doi":"10.1109/BIBE.2018.00019","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00019","url":null,"abstract":"Multiple sequence alignment (MSA) is one of the best studied problems in bioinformatics because of the broad set of genomics, proteomics, and evolutionary analyses that rely on it. Yet the problem is NP-hard and existing heuristics are imperfect. Reinforcement learning (RL) techniques have emerged recently as a potential solution to a wide diversity of computational problems, but have yet to be applied to MSA. In this paper, we describe RLALIGN, a method to solve the MSA problem using RL. RLALIGN is based on Asynchronous Advantage Actor Critic (A3C), a cutting-edge RL framework. Due to the absence of a goal state, however, it required several important modifications. RLALIGN can be trained to accurately align moderate-length sequences, and various heuristics allow it to scale to longer sequences. The accuracy of the alignments produced is on par with, and often better than those of well established alignment algorithms. Overall, our work demonstrates the potential of RL approaches for complex combinatorial problems such as MSA. RLALIGN will prove useful for realignment tasks, where portions of a larger alignment need to be optimized. Unlike classical algorithms, RLALIGN is incognizant to the nature of the scoring scheme, leading to easy generalization to a variety of problem variants.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127707092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ying-Feng Hsu, Morito Matsuoka, Nicolas Jung, Y. Matsumoto, D. Motooka, S. Nakamura
With the continual growth of low-cost and high-throughput DNA sequence technology, the scale and amount of next-generation sequencing (NGS) datasets are continually increasing in many genomics research areas. Shotgun metagenomics sequencing provides comprehensive information on microorganisms, based on complex samples of the ecosystem. Due to challenges of its scale and computational complexity, efficient sequence processing and analyzing tools are needed. In this paper, we propose a novel high-performance shotgun metagenomics sequence analysis engine for the task of sequence comparison. It includes two major components. First, a customized shifting database, which is optimized from any existing DNA sequence dataset. Second, a high-performance sequence computation algorithm that utilizes the customized shifting reference database and accelerates GPU parallel computing. We elaborated upon the efficiency and computational complexity of our proposed approach in an HPC server, which has eight Nvidia Tesla P100 GPUs. We also conducted a case study to detect viral sequences from patients' blood samples. Our experimental result shows that we obtain similar accuracy to the conventional BLAST method, but with a computational speed that is about twenty times faster.
随着低成本、高通量DNA测序技术的不断发展，下一代测序(NGS)数据集的规模和数量在许多基因组学研究领域不断增加。霰弹枪宏基因组测序基于生态系统的复杂样本，提供了关于微生物的全面信息。由于其规模和计算复杂度的挑战，需要高效的序列处理和分析工具。本文提出了一种新型的高性能霰弹枪宏基因组序列分析引擎，用于序列比较。它包括两个主要组成部分。首先，根据现有的DNA序列数据集进行优化，建立自定义的移位数据库。其次，利用自定义移位参考数据库加速GPU并行计算的高性能序列计算算法。我们在HPC服务器上详细阐述了我们提出的方法的效率和计算复杂性，该服务器具有8个Nvidia Tesla P100 gpu。我们还进行了一个病例研究，从患者血液样本中检测病毒序列。我们的实验结果表明，我们获得了与传统BLAST方法相似的精度，但计算速度快了大约20倍。
{"title":"[Regular Paper] A High-Performance Sequence Analysis Engine for Shotgun Metagenomics through GPU Acceleration","authors":"Ying-Feng Hsu, Morito Matsuoka, Nicolas Jung, Y. Matsumoto, D. Motooka, S. Nakamura","doi":"10.1109/BIBE.2018.00018","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00018","url":null,"abstract":"With the continual growth of low-cost and high-throughput DNA sequence technology, the scale and amount of next-generation sequencing (NGS) datasets are continually increasing in many genomics research areas. Shotgun metagenomics sequencing provides comprehensive information on microorganisms, based on complex samples of the ecosystem. Due to challenges of its scale and computational complexity, efficient sequence processing and analyzing tools are needed. In this paper, we propose a novel high-performance shotgun metagenomics sequence analysis engine for the task of sequence comparison. It includes two major components. First, a customized shifting database, which is optimized from any existing DNA sequence dataset. Second, a high-performance sequence computation algorithm that utilizes the customized shifting reference database and accelerates GPU parallel computing. We elaborated upon the efficiency and computational complexity of our proposed approach in an HPC server, which has eight Nvidia Tesla P100 GPUs. We also conducted a case study to detect viral sequences from patients' blood samples. Our experimental result shows that we obtain similar accuracy to the conventional BLAST method, but with a computational speed that is about twenty times faster.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127761950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
More than 90% of malignant tumors in the head and neck are squamous carcinomas. These patients are with an average survival rate of about 5 years. However, some of the head and neck cancer(HNC) patients had the poor survival rate because of development of second primary tumors. In this study, the sequencing was performed using the Illumina system and Sanger sequencing was used to validate all identified mutations. We analyzed primary and second primary tumors in HNC and identified 23 mutant verification only in second primary tumors; 32 mutant verification only in primary tumors; 38 mutant verification in both of them. This mutant verification only in second primary tumors might be the cause of the second primary oral cancer.
{"title":"Mutation Analysis of Second Primary Tumors in the Head and Neck Cancer by Next Generation Sequencing","authors":"Ting-Yuan Liu, Chien-Chin Lee, Hsi-Yuan Huang, Jan-Gowth Chang","doi":"10.1109/BIBE.2018.00068","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00068","url":null,"abstract":"More than 90% of malignant tumors in the head and neck are squamous carcinomas. These patients are with an average survival rate of about 5 years. However, some of the head and neck cancer(HNC) patients had the poor survival rate because of development of second primary tumors. In this study, the sequencing was performed using the Illumina system and Sanger sequencing was used to validate all identified mutations. We analyzed primary and second primary tumors in HNC and identified 23 mutant verification only in second primary tumors; 32 mutant verification only in primary tumors; 38 mutant verification in both of them. This mutant verification only in second primary tumors might be the cause of the second primary oral cancer.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128991356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexandre Krebs, V. Camilo, E. Touati, Y. Benezeth, V. Michel, G. Jouvion, Fan Yang, D. Lamarque, F. Marzani
Spectral acquisitions contain rich information and thus, are promising modalities for early detection of gastric diseases. In this study, we analyze the diffuse reflectance of the gastric inflammatory lesions induced by the bacterium H. pylori in the mouse stomach. A pipeline has been designed to characterize and classify spectra acquired on mice. The pipeline is based on a band clustering algorithm followed by the computation of meaningful division and subtraction features and by classification with a linear SVM classifier. Currently, the pipeline is able to recognize inflamed stomach's spectra with an accuracy of 98%. These results are promising and the same pipeline could be adapted for the study of gastric pathologies in humans.
{"title":"[Regular Paper] Detection of H. pylori Induced Gastric Inflammation by Diffuse Reflectance Analysis","authors":"Alexandre Krebs, V. Camilo, E. Touati, Y. Benezeth, V. Michel, G. Jouvion, Fan Yang, D. Lamarque, F. Marzani","doi":"10.1109/BIBE.2018.00063","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00063","url":null,"abstract":"Spectral acquisitions contain rich information and thus, are promising modalities for early detection of gastric diseases. In this study, we analyze the diffuse reflectance of the gastric inflammatory lesions induced by the bacterium H. pylori in the mouse stomach. A pipeline has been designed to characterize and classify spectra acquired on mice. The pipeline is based on a band clustering algorithm followed by the computation of meaningful division and subtraction features and by classification with a linear SVM classifier. Currently, the pipeline is able to recognize inflamed stomach's spectra with an accuracy of 98%. These results are promising and the same pipeline could be adapted for the study of gastric pathologies in humans.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127159727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of this study was to examine features of attention and concentration based on differences in the amount of unnecessary information using multiple psycho-physiological evaluations. We used the Roken Arousal Scale as a subjective evaluation, and electroencephalograms (alpha attenuation coefficient (AAC), P300) and near-infrared spectroscopy (oxygenated hemoglobin) as physiological indices. To investigate the psycho-physiological differences due to differences in the amount of unnecessary information, we used the oddball paradigm task. As the number of non-target stimuli increased, the oxygenated hemoglobin concentration increased and the P300 amplitude and AAC value tended to decrease. In conclusion, when the amount of unnecessary information is small, the load on the brain and arousal level of decrease are suppressed, and work can be performed while maintaining attention and concentration.
{"title":"Psycho-Physiological Changes Depend on Differences in the Presentation Ratio of Non-target Stimuli","authors":"Hiroaki Yoshikawa, H. Hagiwara","doi":"10.1109/BIBE.2018.00054","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00054","url":null,"abstract":"The purpose of this study was to examine features of attention and concentration based on differences in the amount of unnecessary information using multiple psycho-physiological evaluations. We used the Roken Arousal Scale as a subjective evaluation, and electroencephalograms (alpha attenuation coefficient (AAC), P300) and near-infrared spectroscopy (oxygenated hemoglobin) as physiological indices. To investigate the psycho-physiological differences due to differences in the amount of unnecessary information, we used the oddball paradigm task. As the number of non-target stimuli increased, the oxygenated hemoglobin concentration increased and the P300 amplitude and AAC value tended to decrease. In conclusion, when the amount of unnecessary information is small, the load on the brain and arousal level of decrease are suppressed, and work can be performed while maintaining attention and concentration.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121882349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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